CN113111843A - Remote image data acquisition method and system - Google Patents
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Abstract
The invention provides a remote image data acquisition method and a system, wherein the method comprises the following steps: receiving a target object input by a user; acquiring a target acquisition node set associated with a target object; acquiring a target image through each target acquisition node in a target acquisition node set; acquiring a splicing rule corresponding to a target acquisition node set; and splicing the target images based on the splicing rule to serve as image data and then outputting the image data. According to the remote image data acquisition method and system, the target images related to the target object are automatically acquired according to the target object input by the user, and then the target images are spliced and output according to the corresponding splicing rule, so that the convenience of remotely calling the monitoring image is improved, meanwhile, the camera equipment does not need to be manually inquired, the images acquired by the camera equipment do not need to be manually spliced, the labor cost is reduced, and the user experience is improved.
Description
Technical Field
The invention relates to the technical field of data acquisition, in particular to a remote image data acquisition method and system.
Background
At present, when a monitoring image of a certain place is called, all camera devices related to the place need to be manually inquired firstly, an image acquired by each camera device is acquired, then the installation position of each camera is manually inquired, and each image is spliced according to the installation position, so that the steps are complex, and the labor cost is high.
Disclosure of Invention
One of the objectives of the present invention is to provide a method and a system for remotely acquiring image data, wherein each target image associated with a target object is automatically acquired according to the target object input by a user, and each target image is spliced according to a corresponding splicing rule and then output, so that convenience in remotely retrieving a monitoring image is improved, and meanwhile, a camera device does not need to be manually queried, and images acquired by each camera device do not need to be manually spliced, so that labor cost is reduced, and user experience is improved.
The embodiment of the invention provides a remote image data acquisition method, which comprises the following steps:
receiving a target object input by a user;
acquiring a target acquisition node set associated with a target object;
acquiring a target image through each target acquisition node in a target acquisition node set;
acquiring a splicing rule corresponding to a target acquisition node set;
and splicing the target images based on the splicing rule to serve as image data and then outputting the image data.
Preferably, the acquiring a target collection node set associated with the target object includes:
acquiring a position area corresponding to a target object;
acquiring a preset association rule, and acquiring a first association position area and a second association position area associated with the position area based on the association rule;
acquiring a first acquisition node set corresponding to a position area in a preset acquisition node database;
acquiring a second acquisition node set corresponding to the first associated position area and a third acquisition node set corresponding to the second associated position area in an acquisition node database;
and integrating the first collection node set, the second collection node set and the third collection node set to obtain a target collection node set.
Preferably, before the target image is acquired by each target acquisition node in the target acquisition node set, the method further includes:
verifying the state performance of each target acquisition node in the target acquisition node set, and replacing target acquisition nodes which fail to be verified in the target acquisition node set with corresponding preset standby nodes;
the method for verifying the state performance of each target acquisition node in the target acquisition node set comprises the following steps:
acquiring a preset state performance detection model, detecting each target acquisition node for multiple times by adopting the state performance detection model, outputting a detection feedback value after each detection, and storing the detection feedback value into a preset detection feedback value list;
calculating a first verification index of each target acquisition node based on the detection feedback value list, wherein the calculation formula is as follows:
wherein, mu1,iFirst verification index, α, for the ith target acquisition nodefullIs a preset first verification index full mark threshold value, e is a natural constant, niFor the total number of detection feedback values, σ, corresponding to the ith target acquisition node in the list of detection feedback valuesi,tFor the t-th detection feedback value, sigma, in the detection feedback value corresponding to the i-th target acquisition node in the detection feedback value list0Is a preset detection feedback value threshold value;
and when the first verification index is larger than or equal to a preset first verification index threshold, the state performance of the corresponding target acquisition node passes the verification, otherwise, the state performance of the corresponding target acquisition node does not pass the verification.
Preferably, when a target image is acquired through each target acquisition node in the target acquisition node set, the current state value and the state value record of each target acquisition node are acquired, a preset prediction model is acquired at the same time, the prediction model is adopted to predict the prediction state value of the corresponding target acquisition node based on the state value record, the stability of each target acquisition node is verified based on the current state value and the prediction state value, and if the target acquisition node which is not verified is available, a user is timely reminded;
the method for verifying the stability of each target acquisition node based on the current state value and the predicted state value comprises the following steps:
calculating a second validation index of each target acquisition node based on the current state value and the predicted state value, wherein the calculation formula is as follows:
wherein, mu2,iSecond verification index, rho, for the ith target acquisition nodefullIs a preset second verification index full mark threshold value, e is a natural constant, diTotal number of times for predicting the state value record corresponding to the ith target collection node using a prediction model, zi,0Is the current state value, z, of the ith target acquisition nodei,xA predicted state value Z obtained by performing the x-th prediction on the state value record corresponding to the i-th target acquisition node by using a prediction model0Is a preset detection threshold value, tau is a preset error coefficient, epsilon1And ε2The weight value is a preset weight value;
and when the second verification index is larger than or equal to a preset second verification index threshold value, the stability of the corresponding target acquisition node passes the verification, otherwise, the stability of the corresponding target acquisition node does not pass the verification.
Preferably, before the target image is acquired by each target acquisition node in the target acquisition node set, the method further includes:
verifying the effectiveness of each target collection node in the target collection node set, and removing target collection nodes which fail to be verified in the target collection nodes;
the verifying the validity of each target acquisition node in the target acquisition node set comprises the following steps:
taking any one target acquisition node in the target acquisition node set as a first node, and simultaneously taking another target acquisition node in the target acquisition node set as a second node;
verifying the first node and the second node;
respectively acquiring a first visual angle area of a first node and a second visual angle area of a second node;
acquiring the overlapping rate of the first visual angle area and the second visual angle area;
respectively acquiring a first characteristic value of a first node and a second characteristic value of a second node;
simultaneously and respectively acquiring a first suitable range of the first node and a second suitable range of the second node;
respectively adjusting a first upper limit value of the first proper range and a second upper limit value of the second proper range according to a preset upper adjustment range;
simultaneously, respectively adjusting a first lower limit value of the first proper range and a second lower limit value of the second proper range according to a preset down-regulation amplitude;
respectively acquiring a first adjusting range after the first suitable range is adjusted and a second adjusting range after the second suitable range is adjusted;
when the overlapping rate is less than or equal to a preset overlapping rate threshold value, the first characteristic value is within a first adjusting range, and the second characteristic value is within a second adjusting range, the first node and the second node pass the verification, and the first node and the next second node continue to be verified;
and when the first node and all the second nodes in the target collection node set pass the verification, the validity of the first node passes the verification.
The embodiment of the invention provides a remote image data acquisition system, which comprises:
the receiving module is used for receiving a target object input by a user;
the first acquisition module is used for acquiring a target acquisition node set associated with a target object;
the second acquisition module is used for acquiring a target image through each target acquisition node in the target acquisition node set;
the third acquisition module is used for acquiring the splicing rule corresponding to the target acquisition node set;
and the splicing and output module is used for splicing all the target images based on the splicing rule to serve as image data and then outputting the image data.
Preferably, the first obtaining module performs operations including:
acquiring a position area corresponding to a target object;
acquiring a preset association rule, and acquiring a first association position area and a second association position area associated with the position area based on the association rule;
acquiring a first acquisition node set corresponding to a position area in a preset acquisition node database;
acquiring a second acquisition node set corresponding to the first associated position area and a third acquisition node set corresponding to the second associated position area in an acquisition node database;
and integrating the first collection node set, the second collection node set and the third collection node set to obtain a target collection node set.
Preferably, the system for remotely acquiring image data further comprises:
the verification and replacement module is used for verifying the state performance of each target acquisition node in the target acquisition node set before acquiring a target image through each target acquisition node in the target acquisition node set, and replacing target acquisition nodes which fail to be verified in the target acquisition node set with corresponding preset standby nodes;
the verification and replacement module performs operations comprising:
acquiring a preset state performance detection model, detecting each target acquisition node for multiple times by adopting the state performance detection model, outputting a detection feedback value after each detection, and storing the detection feedback value into a preset detection feedback value list;
calculating a first verification index of each target acquisition node based on the detection feedback value list, wherein the calculation formula is as follows:
wherein, mu1,iCollecting nodes for ith targetFirst verification index of (a)fullIs a preset first verification index full mark threshold value, e is a natural constant, niFor the total number of detection feedback values, σ, corresponding to the ith target acquisition node in the list of detection feedback valuesi,tFor the t-th detection feedback value, sigma, in the detection feedback value corresponding to the i-th target acquisition node in the detection feedback value list0Is a preset detection feedback value threshold value;
and when the first verification index is larger than or equal to a preset first verification index threshold, the state performance of the corresponding target acquisition node passes the verification, otherwise, the state performance of the corresponding target acquisition node does not pass the verification.
Preferably, the system for remotely acquiring image data further comprises:
the stability verification module is used for acquiring a current state value and a state value record of each target acquisition node when a target image is acquired through each target acquisition node in the target acquisition node set, acquiring a preset prediction model at the same time, predicting a prediction state value of the corresponding target acquisition node based on the state value record by adopting the prediction model, verifying the stability of each target acquisition node based on the current state value and the prediction state value, and timely reminding a user if a target acquisition node which cannot be verified exists;
the stability verification module performs operations comprising:
calculating a second validation index of each target acquisition node based on the current state value and the predicted state value, wherein the calculation formula is as follows:
wherein, mu2,iSecond verification index, rho, for the ith target acquisition nodefullIs a preset second verification index full mark threshold value, e is a natural constant, diTotal number of times for predicting the state value record corresponding to the ith target collection node using a prediction model, zi,0Is the current state value, z, of the ith target acquisition nodei,xFor the shape corresponding to the ith target acquisition node using a predictive modelThe state value records the predicted state value, Z, obtained by the x-th prediction0Is a preset detection threshold value, tau is a preset error coefficient, epsilon1And ε2The weight value is a preset weight value;
and when the second verification index is larger than or equal to a preset second verification index threshold value, the stability of the corresponding target acquisition node passes the verification, otherwise, the stability of the corresponding target acquisition node does not pass the verification.
Preferably, the system for remotely acquiring image data further comprises:
the verification and rejection module is used for verifying the effectiveness of each target acquisition node in the target acquisition node set before acquiring a target image through each target acquisition node in the target acquisition node set and rejecting target acquisition nodes which fail to be verified in the target acquisition nodes;
the verification and rejection module executes the following operations:
taking any one target acquisition node in the target acquisition node set as a first node, and simultaneously taking another target acquisition node in the target acquisition node set as a second node;
verifying the first node and the second node;
respectively acquiring a first visual angle area of a first node and a second visual angle area of a second node;
acquiring the overlapping rate of the first visual angle area and the second visual angle area;
respectively acquiring a first characteristic value of a first node and a second characteristic value of a second node;
simultaneously and respectively acquiring a first suitable range of the first node and a second suitable range of the second node;
respectively adjusting a first upper limit value of the first proper range and a second upper limit value of the second proper range according to a preset upper adjustment range;
simultaneously, respectively adjusting a first lower limit value of the first proper range and a second lower limit value of the second proper range according to a preset down-regulation amplitude;
respectively acquiring a first adjusting range after the first suitable range is adjusted and a second adjusting range after the second suitable range is adjusted;
when the overlapping rate is less than or equal to a preset overlapping rate threshold value, the first characteristic value is within a first adjusting range, and the second characteristic value is within a second adjusting range, the first node and the second node pass the verification, and the first node and the next second node continue to be verified;
and when the first node and all the second nodes in the target collection node set pass the verification, the validity of the first node passes the verification.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a method for remotely acquiring image data according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a remote image data acquisition system according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides a remote image data acquisition method, as shown in fig. 1, comprising the following steps:
s1, receiving a target object input by a user;
s2, acquiring a target collection node set associated with the target object;
s3, acquiring a target image through each target acquisition node in the target acquisition node set;
s4, acquiring a splicing rule corresponding to the target collection node set;
and S5, splicing the target images based on the splicing rule to obtain image data and outputting the image data.
The working principle of the technical scheme is as follows:
a user inputs a target object (such as a certain street center position and the like) by operating an intelligent terminal (such as a smart phone, a tablet or a computer and the like); acquiring a target acquisition node set associated with the target object (for example, a node at which each monitoring device is connected with a server within a certain distance from the front to the back of the corresponding street at the center of the street); acquiring a target image (namely a street image shot by each monitoring device) through each target acquisition node in the target acquisition node set; acquiring a splicing rule corresponding to the target acquisition node set (for example, a spatial position relation between acquired images of each monitoring device determined according to the installation position of the monitoring device corresponding to each target acquisition node in the target acquisition node set); and splicing the acquired target images to obtain image data.
The beneficial effects of the above technical scheme are: according to the embodiment of the invention, each target image associated with the target object is automatically acquired according to the target object input by the user, and then each target image is spliced and output according to the corresponding splicing rule, so that the convenience of remotely calling the monitoring image is improved, meanwhile, the camera equipment does not need to be manually inquired, and the images acquired by the camera equipment do not need to be manually spliced, so that the labor cost is reduced, and the user experience is improved.
The embodiment of the invention provides a remote image data acquisition method, which acquires a target acquisition node set associated with a target object and comprises the following steps:
acquiring a position area corresponding to a target object;
acquiring a preset association rule, and acquiring a first association position area and a second association position area associated with the position area based on the association rule;
acquiring a first acquisition node set corresponding to a position area in a preset acquisition node database;
acquiring a second acquisition node set corresponding to the first associated position area and a third acquisition node set corresponding to the second associated position area in an acquisition node database;
and integrating the first collection node set, the second collection node set and the third collection node set to obtain a target collection node set.
The working principle of the technical scheme is as follows:
acquiring a position area (a coordinate set formed by combining a plurality of position coordinates) corresponding to a target object (a certain street center position); the preset association rule is specifically as follows: for example, a position area of the street center position within a certain range around the street is selected as a relevant position area; the preset collection node database specifically comprises: and the node and the installation position corresponding to each monitoring device.
The beneficial effects of the above technical scheme are: according to the embodiment of the invention, the first associated position area and the second associated position area associated with the position area corresponding to the target object are automatically obtained, and the nodes of the monitoring equipment related to the position area, the first associated position area and the second associated position area are integrated to be used as the target collection node set, so that the working efficiency of the system is improved.
The embodiment of the invention provides a remote image data acquisition method, which comprises the following steps that before each target acquisition node in a target acquisition node set acquires a target image:
verifying the state performance of each target acquisition node in the target acquisition node set, and replacing target acquisition nodes which fail to be verified in the target acquisition node set with corresponding preset standby nodes;
the method for verifying the state performance of each target acquisition node in the target acquisition node set comprises the following steps:
acquiring a preset state performance detection model, detecting each target acquisition node for multiple times by adopting the state performance detection model, outputting a detection feedback value after each detection, and storing the detection feedback value into a preset detection feedback value list;
calculating a first verification index of each target acquisition node based on the detection feedback value list, wherein the calculation formula is as follows:
wherein, mu1,iFirst verification index, α, for the ith target acquisition nodefullIs a preset first verification index full mark threshold value, e is a natural constant, niFor the total number of detection feedback values, σ, corresponding to the ith target acquisition node in the list of detection feedback valuesi,tFor the t-th detection feedback value, sigma, in the detection feedback value corresponding to the i-th target acquisition node in the detection feedback value list0Is a preset detection feedback value threshold value;
and when the first verification index is larger than or equal to a preset first verification index threshold, the state performance of the corresponding target acquisition node passes the verification, otherwise, the state performance of the corresponding target acquisition node does not pass the verification.
The working principle of the technical scheme is as follows:
each monitoring device is provided with at least 2 nodes (a main node and a plurality of standby nodes) when being connected with a server, the state performance of the main node is poor and needs to be switched to the standby nodes in time due to the fact that a plurality of users who call images acquired by a certain monitoring device through the main node at the same time point are possibly caused, generally, the performance of the main node can completely load most application scenes and does not need to be switched to the standby nodes, therefore, the performance of the standby nodes is completely reliable, the users can also set to verify the state performance of the standby nodes, and if the verification fails, the standby nodes are switched to the next standby node, and the like; the preset state performance detection model specifically comprises the following steps: the model is generated by learning a large number of recorded samples for manually detecting the state performance of the node by using a machine learning algorithm, the current state performance of the node can be detected for multiple times in a short time according to parameters such as time delay, transmission rate and throughput of the node and a large number of historical parameters of the node, and a detection feedback value is output after each detection; the preset detection feedback value list is used for storing detection feedback values output after each detection of the state performance detection model; based on the first verification index of each target acquisition node of the detection feedback value list technology, the larger the first verification index is, the better the state performance of the corresponding target acquisition node is, otherwise, the smaller the first verification index is, the worse the state performance of the corresponding target acquisition node is; the preset first verification index full-scale threshold specifically comprises: for example, 100; the preset detection feedback value threshold specifically comprises: for example, 98.5; the preset first verification index threshold specifically includes: for example, 95.
The beneficial effects of the above technical scheme are: according to the embodiment of the invention, when the target image is acquired through each target acquisition node in the target acquisition node set, the state performance of each target acquisition node is verified in advance, and the target acquisition nodes which do not pass the verification are replaced by corresponding nodes for the preset equipment, so that the stability of acquiring the target image through each target acquisition node is ensured, the quality of the acquired target image is further ensured, meanwhile, the first verification index is calculated by using the formula, whether the state performance of the target acquisition node reaches the standard or not can be judged quickly, the working efficiency of the system is improved, and the system is more intelligent.
The embodiment of the invention provides a remote image data acquisition method, which comprises the steps of acquiring a current state value and a state value record of each target acquisition node when each target acquisition node in a target acquisition node set acquires a target image, acquiring a preset prediction model at the same time, predicting a prediction state value of the corresponding target acquisition node based on the state value record by adopting the prediction model, verifying the stability of each target acquisition node based on the current state value and the prediction state value, and timely reminding a user if the target acquisition node which cannot be verified exists;
the method for verifying the stability of each target acquisition node based on the current state value and the predicted state value comprises the following steps:
calculating a second validation index of each target acquisition node based on the current state value and the predicted state value, wherein the calculation formula is as follows:
wherein, mu2,iSecond verification index, rho, for the ith target acquisition nodefullIs a preset second verification index full mark threshold value, e is a natural constant, diTotal number of times for predicting the state value record corresponding to the ith target collection node using a prediction model, zi,0Is the current state value, z, of the ith target acquisition nodei,xA predicted state value Z obtained by performing the x-th prediction on the state value record corresponding to the i-th target acquisition node by using a prediction model0Is a preset detection threshold value, tau is a preset error coefficient, epsilon1And ε2The weight value is a preset weight value;
and when the second verification index is larger than or equal to a preset second verification index threshold value, the stability of the corresponding target acquisition node passes the verification, otherwise, the stability of the corresponding target acquisition node does not pass the verification.
The working principle of the technical scheme is as follows:
the preset prediction model specifically comprises: a model generated by learning the state value records of a large number of nodes by using a machine learning algorithm; the stability of a certain target acquisition node is verified based on the current state value of the target acquisition node and a prediction state value which is output by learning a state value record by using a prediction model, when the target acquisition node which is not verified exists, a user is reminded, the user can manually set the target acquisition node, more network resources are called to be distributed to the target acquisition node, the user can also trigger a self-adaptive adjustment mode, and the system automatically switches the target acquisition node which is not verified to a standby node; based on the second verification index of each target acquisition node of the current state value and predicted state value technology, the larger the second verification index is, the higher the stability of the target acquisition node is, otherwise, the smaller the second verification index is, the lower the stability of the target acquisition node is; the preset second verification index full-scale threshold specifically comprises: for example, 100; the preset detection threshold specifically comprises: for example, 98; the prediction model is used for prediction, certain errors exist, and therefore an error coefficient needs to be introduced; the preset second validation index threshold specifically includes: for example, 95.
The beneficial effects of the above technical scheme are: according to the embodiment of the invention, when the target image is acquired through each target acquisition node in the target acquisition node set, the stability of each target acquisition node is verified, when the target acquisition node which is not verified exists, the user is correspondingly reminded, so that the user can conveniently and timely take corresponding measures, the stability of remotely acquiring image data is ensured, meanwhile, the user does not need to search by himself, the convenience is improved, in addition, the second verification index of each target acquisition node is calculated through the formula, whether the stability of each target acquisition node reaches the standard or not can be quickly determined, the working efficiency of the system is improved, and the system is more intelligent.
The embodiment of the invention provides a remote image data acquisition method, which comprises the following steps that before each target acquisition node in a target acquisition node set acquires a target image:
verifying the effectiveness of each target collection node in the target collection node set, and removing target collection nodes which fail to be verified in the target collection nodes;
the verifying the validity of each target acquisition node in the target acquisition node set comprises the following steps:
taking any one target acquisition node in the target acquisition node set as a first node, and simultaneously taking another target acquisition node in the target acquisition node set as a second node;
verifying the first node and the second node;
respectively acquiring a first visual angle area of a first node and a second visual angle area of a second node;
acquiring the overlapping rate of the first visual angle area and the second visual angle area;
respectively acquiring a first characteristic value of a first node and a second characteristic value of a second node;
simultaneously and respectively acquiring a first suitable range of the first node and a second suitable range of the second node;
respectively adjusting a first upper limit value of the first proper range and a second upper limit value of the second proper range according to a preset upper adjustment range;
simultaneously, respectively adjusting a first lower limit value of the first proper range and a second lower limit value of the second proper range according to a preset down-regulation amplitude;
respectively acquiring a first adjusting range after the first suitable range is adjusted and a second adjusting range after the second suitable range is adjusted;
when the overlapping rate is less than or equal to a preset overlapping rate threshold value, the first characteristic value is within a first adjusting range, and the second characteristic value is within a second adjusting range, the first node and the second node pass the verification, and the first node and the next second node continue to be verified;
and when the first node and all the second nodes in the target collection node set pass the verification, the validity of the first node passes the verification.
The working principle of the technical scheme is as follows:
before a target image is acquired, the effectiveness of target acquisition nodes (whether images to be acquired by each node are suitable for splicing) needs to be verified; when the monitoring equipment is installed, a worker sets a viewing angle area (namely a monitoring range) of each monitoring equipment, and sets a characteristic value (such as a size of a collected image) and a suitable range (such as 1 inch to 3 inches) of each monitoring equipment; the preset up-regulation amplitude and the preset down-regulation amplitude are set by a user and depend on the degree of the requirement of the user on the finally spliced image data; if the overlapping rate of the first node and a certain second node corresponding to the view angle area is high, the first node is not verified and needs to be removed, and only the second node is adopted to obtain the target image; if the first characteristic value of the first node does not fall into the second adjustment range after the second suitable range is enlarged and adjusted, the target image of the first node and the target image of the second node corresponding to the second adjustment range are not spliced to the greatest extent, and the splicing effect is poor; the principle that the second characteristic value of the second node does not fall within the first adjustment range after the first suitable range has been enlarged and adjusted is the same as that; the preset overlap rate threshold specifically includes: such as 85.
The beneficial effects of the above technical scheme are: the embodiment of the invention verifies the effectiveness of each target acquisition node in the target acquisition node set, removes the target acquisition nodes which are not verified in the target acquisition nodes, avoids the target acquisition nodes which are not verified in effectiveness from occupying network resources, ensures the splicing quality of splicing among target images, meets the requirements of high-requirement users, sets the preset up-regulation amplitude and the preset down-regulation amplitude which are self-regulated by the users, can meet the requirements of normal users, expands the application range and improves the user experience.
An embodiment of the present invention provides a remote image data acquisition system, as shown in fig. 2, including:
the receiving module 1 is used for receiving a target object input by a user;
the first acquisition module 2 is used for acquiring a target acquisition node set associated with a target object;
the second acquisition module 3 is used for acquiring a target image through each target acquisition node in the target acquisition node set;
the third obtaining module 4 is used for obtaining the splicing rule corresponding to the target collection node set;
and the splicing and output module 5 is used for splicing the target images based on the splicing rule to serve as image data and then outputting the image data.
The working principle of the technical scheme is as follows:
a user inputs a target object (such as a certain street center position and the like) by operating an intelligent terminal (such as a smart phone, a tablet or a computer and the like); acquiring a target acquisition node set associated with the target object (for example, a node at which each monitoring device is connected with a server within a certain distance from the front to the back of the corresponding street at the center of the street); acquiring a target image (namely a street image shot by each monitoring device) through each target acquisition node in the target acquisition node set; acquiring a splicing rule corresponding to the target acquisition node set (for example, a spatial position relation between acquired images of each monitoring device determined according to the installation position of the monitoring device corresponding to each target acquisition node in the target acquisition node set); and splicing the acquired target images to obtain image data.
The beneficial effects of the above technical scheme are: according to the embodiment of the invention, each target image associated with the target object is automatically acquired according to the target object input by the user, and then each target image is spliced and output according to the corresponding splicing rule, so that the convenience of remotely calling the monitoring image is improved, meanwhile, the camera equipment does not need to be manually inquired, and the images acquired by the camera equipment do not need to be manually spliced, so that the labor cost is reduced, and the user experience is improved.
The embodiment of the invention provides a remote acquisition system of image data, wherein a first acquisition module 2 executes the following operations:
acquiring a position area corresponding to a target object;
acquiring a preset association rule, and acquiring a first association position area and a second association position area associated with the position area based on the association rule;
acquiring a first acquisition node set corresponding to a position area in a preset acquisition node database;
acquiring a second acquisition node set corresponding to the first associated position area and a third acquisition node set corresponding to the second associated position area in an acquisition node database;
and integrating the first collection node set, the second collection node set and the third collection node set to obtain a target collection node set.
The working principle of the technical scheme is as follows:
acquiring a position area (a coordinate set formed by combining a plurality of position coordinates) corresponding to a target object (a certain street center position); the preset association rule is specifically as follows: for example, a position area of the street center position within a certain range around the street is selected as a relevant position area; the preset collection node database specifically comprises: and the node and the installation position corresponding to each monitoring device.
The beneficial effects of the above technical scheme are: according to the embodiment of the invention, the first associated position area and the second associated position area associated with the position area corresponding to the target object are automatically obtained, and the nodes of the monitoring equipment related to the position area, the first associated position area and the second associated position area are integrated to be used as the target collection node set, so that the working efficiency of the system is improved.
The embodiment of the invention provides a remote acquisition system of image data, which further comprises:
the verification and replacement module is used for verifying the state performance of each target acquisition node in the target acquisition node set before acquiring a target image through each target acquisition node in the target acquisition node set, and replacing target acquisition nodes which fail to be verified in the target acquisition node set with corresponding preset standby nodes;
the verification and replacement module performs operations comprising:
acquiring a preset state performance detection model, detecting each target acquisition node for multiple times by adopting the state performance detection model, outputting a detection feedback value after each detection, and storing the detection feedback value into a preset detection feedback value list;
calculating a first verification index of each target acquisition node based on the detection feedback value list, wherein the calculation formula is as follows:
wherein, mu1,iFirst verification index, α, for the ith target acquisition nodefullIs a preset first verification index full mark threshold value, e is a natural constant, niFor the total number of detection feedback values, σ, corresponding to the ith target acquisition node in the list of detection feedback valuesi,tFor the t-th detection feedback value, sigma, in the detection feedback value corresponding to the i-th target acquisition node in the detection feedback value list0Is a preset detection feedback value threshold value;
and when the first verification index is larger than or equal to a preset first verification index threshold, the state performance of the corresponding target acquisition node passes the verification, otherwise, the state performance of the corresponding target acquisition node does not pass the verification.
The working principle of the technical scheme is as follows:
each monitoring device is provided with at least 2 nodes (a main node and a plurality of standby nodes) when being connected with a server, the state performance of the main node is poor and needs to be switched to the standby nodes in time due to the fact that a plurality of users who call images acquired by a certain monitoring device through the main node at the same time point are possibly caused, generally, the performance of the main node can completely load most application scenes and does not need to be switched to the standby nodes, therefore, the performance of the standby nodes is completely reliable, the users can also set to verify the state performance of the standby nodes, and if the verification fails, the standby nodes are switched to the next standby node, and the like; the preset state performance detection model specifically comprises the following steps: the model is generated by learning a large number of recorded samples for manually detecting the state performance of the node by using a machine learning algorithm, the current state performance of the node can be detected for multiple times in a short time according to parameters such as time delay, transmission rate and throughput of the node and a large number of historical parameters of the node, and a detection feedback value is output after each detection; the preset detection feedback value list is used for storing detection feedback values output after each detection of the state performance detection model; based on the first verification index of each target acquisition node of the detection feedback value list technology, the larger the first verification index is, the better the state performance of the corresponding target acquisition node is, otherwise, the smaller the first verification index is, the worse the state performance of the corresponding target acquisition node is; the preset first verification index full-scale threshold specifically comprises: for example, 100; the preset detection feedback value threshold specifically comprises: for example, 98.5; the preset first verification index threshold specifically includes: for example, 95.
The beneficial effects of the above technical scheme are: according to the embodiment of the invention, when the target image is acquired through each target acquisition node in the target acquisition node set, the state performance of each target acquisition node is verified in advance, and the target acquisition nodes which do not pass the verification are replaced by corresponding nodes for the preset equipment, so that the stability of acquiring the target image through each target acquisition node is ensured, the quality of the acquired target image is further ensured, meanwhile, the first verification index is calculated by using the formula, whether the state performance of the target acquisition node reaches the standard or not can be judged quickly, the working efficiency of the system is improved, and the system is more intelligent.
The embodiment of the invention provides a remote acquisition system of image data, which further comprises:
the stability verification module is used for acquiring a current state value and a state value record of each target acquisition node when a target image is acquired through each target acquisition node in the target acquisition node set, acquiring a preset prediction model at the same time, predicting a prediction state value of the corresponding target acquisition node based on the state value record by adopting the prediction model, verifying the stability of each target acquisition node based on the current state value and the prediction state value, and timely reminding a user if a target acquisition node which cannot be verified exists;
the stability verification module performs operations comprising:
calculating a second validation index of each target acquisition node based on the current state value and the predicted state value, wherein the calculation formula is as follows:
wherein, mu2,iSecond verification index, rho, for the ith target acquisition nodefullIs a preset second verification index full mark threshold value, e is a natural constant, diTotal number of times for predicting the state value record corresponding to the ith target collection node using a prediction model, zi,0Is the current state value, z, of the ith target acquisition nodei,xA predicted state value Z obtained by performing the x-th prediction on the state value record corresponding to the i-th target acquisition node by using a prediction model0Is a preset detection threshold value, tau is a preset error coefficient, epsilon1And ε2The weight value is a preset weight value;
and when the second verification index is larger than or equal to a preset second verification index threshold value, the stability of the corresponding target acquisition node passes the verification, otherwise, the stability of the corresponding target acquisition node does not pass the verification.
The working principle of the technical scheme is as follows:
the preset prediction model specifically comprises: a model generated by learning the state value records of a large number of nodes by using a machine learning algorithm; the stability of a certain target acquisition node is verified based on the current state value of the target acquisition node and a prediction state value which is output by learning a state value record by using a prediction model, when the target acquisition node which is not verified exists, a user is reminded, the user can manually set the target acquisition node, more network resources are called to be distributed to the target acquisition node, the user can also trigger a self-adaptive adjustment mode, and the system automatically switches the target acquisition node which is not verified to a standby node; based on the second verification index of each target acquisition node of the current state value and predicted state value technology, the larger the second verification index is, the higher the stability of the target acquisition node is, otherwise, the smaller the second verification index is, the lower the stability of the target acquisition node is; the preset second verification index full-scale threshold specifically comprises: for example, 100; the preset detection threshold specifically comprises: for example, 98; the prediction model is used for prediction, certain errors exist, and therefore an error coefficient needs to be introduced; the preset second validation index threshold specifically includes: for example, 95.
The beneficial effects of the above technical scheme are: according to the embodiment of the invention, when the target image is acquired through each target acquisition node in the target acquisition node set, the stability of each target acquisition node is verified, when the target acquisition node which is not verified exists, the user is correspondingly reminded, so that the user can conveniently and timely take corresponding measures, the stability of remotely acquiring image data is ensured, meanwhile, the user does not need to search by himself, the convenience is improved, in addition, the second verification index of each target acquisition node is calculated through the formula, whether the stability of each target acquisition node reaches the standard or not can be quickly determined, the working efficiency of the system is improved, and the system is more intelligent.
The embodiment of the invention provides a remote acquisition system of image data, which further comprises:
the verification and rejection module is used for verifying the effectiveness of each target acquisition node in the target acquisition node set before acquiring a target image through each target acquisition node in the target acquisition node set and rejecting target acquisition nodes which fail to be verified in the target acquisition nodes;
the verification and rejection module executes the following operations:
taking any one target acquisition node in the target acquisition node set as a first node, and simultaneously taking another target acquisition node in the target acquisition node set as a second node;
verifying the first node and the second node;
respectively acquiring a first visual angle area of a first node and a second visual angle area of a second node;
acquiring the overlapping rate of the first visual angle area and the second visual angle area;
respectively acquiring a first characteristic value of a first node and a second characteristic value of a second node;
simultaneously and respectively acquiring a first suitable range of the first node and a second suitable range of the second node;
respectively adjusting a first upper limit value of the first proper range and a second upper limit value of the second proper range according to a preset upper adjustment range;
simultaneously, respectively adjusting a first lower limit value of the first proper range and a second lower limit value of the second proper range according to a preset down-regulation amplitude;
respectively acquiring a first adjusting range after the first suitable range is adjusted and a second adjusting range after the second suitable range is adjusted;
when the overlapping rate is less than or equal to a preset overlapping rate threshold value, the first characteristic value is within a first adjusting range, and the second characteristic value is within a second adjusting range, the first node and the second node pass the verification, and the first node and the next second node continue to be verified;
and when the first node and all the second nodes in the target collection node set pass the verification, the validity of the first node passes the verification.
The working principle of the technical scheme is as follows:
before a target image is acquired, the effectiveness of target acquisition nodes (whether images to be acquired by each node are suitable for splicing) needs to be verified; when the monitoring equipment is installed, a worker sets a viewing angle area (namely a monitoring range) of each monitoring equipment, and sets a characteristic value (such as a size of a collected image) and a suitable range (such as 1 inch to 3 inches) of each monitoring equipment; the preset up-regulation amplitude and the preset down-regulation amplitude are set by a user and depend on the degree of the requirement of the user on the finally spliced image data; if the overlapping rate of the first node and a certain second node corresponding to the view angle area is high, the first node is not verified and needs to be removed, and only the second node is adopted to obtain the target image; if the first characteristic value of the first node does not fall into the second adjustment range after the second suitable range is enlarged and adjusted, the target image of the first node and the target image of the second node corresponding to the second adjustment range are not spliced to the greatest extent, and the splicing effect is poor; the principle that the second characteristic value of the second node does not fall within the first adjustment range after the first suitable range has been enlarged and adjusted is the same as that; the preset overlap rate threshold specifically includes: such as 85.
The beneficial effects of the above technical scheme are: the embodiment of the invention verifies the effectiveness of each target acquisition node in the target acquisition node set, removes the target acquisition nodes which are not verified in the target acquisition nodes, avoids the target acquisition nodes which are not verified in effectiveness from occupying network resources, ensures the splicing quality of splicing among target images, meets the requirements of high-requirement users, sets the preset up-regulation amplitude and the preset down-regulation amplitude which are self-regulated by the users, can meet the requirements of normal users, expands the application range and improves the user experience.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for remotely acquiring image data, comprising:
receiving a target object input by a user;
acquiring a target acquisition node set associated with the target object;
acquiring a target image through each target acquisition node in the target acquisition node set;
acquiring a splicing rule corresponding to the target acquisition node set;
and splicing the target images based on the splicing rule to serve as image data and then outputting the image data.
2. The method of claim 1, wherein obtaining a set of target acquisition nodes associated with the target object comprises:
acquiring a position area corresponding to the target object;
acquiring a preset association rule, and acquiring a first association position area and a second association position area associated with the position area based on the association rule;
acquiring a first acquisition node set corresponding to the position area in a preset acquisition node database;
acquiring a second acquisition node set corresponding to the first associated position area and a third acquisition node set corresponding to the second associated position area in the acquisition node database;
and integrating the first collection node set, the second collection node set and the third collection node set to obtain the target collection node set.
3. The method of claim 1, wherein before the target image is acquired by each target acquisition node in the set of target acquisition nodes, the method further comprises:
verifying the state performance of each target acquisition node in the target acquisition node set, and replacing target acquisition nodes which fail to be verified in the target acquisition node set with corresponding preset standby nodes;
verifying the state performance of each target acquisition node in the target acquisition node set, wherein the verifying the state performance of each target acquisition node in the target acquisition node set comprises the following steps:
acquiring a preset state performance detection model, detecting each target acquisition node for multiple times by adopting the state performance detection model, outputting a detection feedback value after each detection, and storing the detection feedback value into a preset detection feedback value list;
calculating a first verification index of each target acquisition node based on the detection feedback value list, wherein the calculation formula is as follows:
wherein, mu1,iThe first verification index, α, for the ith target collection nodefullIs a preset first verification index full mark threshold value, e is a natural constant, niThe total number, sigma, of the detection feedback values corresponding to the ith target collection node in the detection feedback value listi,tFor the t-th detection feedback value, sigma, in the detection feedback values corresponding to the ith target acquisition node in the detection feedback value list0Is a preset detection feedback value threshold value;
and when the first verification index is larger than or equal to a preset first verification index threshold value, the state performance corresponding to the target acquisition node passes the verification, otherwise, the state performance does not pass the verification.
4. The method according to claim 1, wherein when a target image is acquired by each target acquisition node in the set of target acquisition nodes, a current state value and a state value record of each target acquisition node are acquired, a preset prediction model is acquired at the same time, the prediction model is used for predicting a prediction state value corresponding to the target acquisition node based on the state value record, the stability of each target acquisition node is verified based on the current state value and the prediction state value, and if the target acquisition node which fails in verification exists, a user is prompted in time;
wherein verifying the stability of each target collection node based on the current state value and the predicted state value comprises:
calculating a second validation index for each of the target collection nodes based on the current state value and the predicted state value, the calculation formula being as follows:
wherein, mu2,iThe second verification index, p, for the ith target acquisition nodefullIs a preset second verification index full mark threshold value, e is a natural constant, diZ is the total number of times the status value record corresponding to the ith target collection node is predicted using the prediction modeli,0For the current state value, z, of the ith target collection nodei,xThe predicted state value Z obtained by performing the x-th prediction on the state value record corresponding to the ith target acquisition node by using the prediction model0Is a preset detection threshold value, tau is a preset error coefficient, epsilon1And ε2The weight value is a preset weight value;
and when the second verification index is larger than or equal to a preset second verification index threshold value, the stability corresponding to the target acquisition node passes the verification, otherwise, the stability does not pass the verification.
5. The method of claim 1, wherein before the target image is acquired by each target acquisition node in the set of target acquisition nodes, the method further comprises:
verifying the effectiveness of each target collection node in the target collection node set, and removing the target collection nodes which fail to be verified in the target collection nodes;
verifying the validity of each target acquisition node in the target acquisition node set, wherein the verifying comprises the following steps:
taking any one target acquisition node in the target acquisition node set as a first node, and simultaneously taking another target acquisition node in the target acquisition node set as a second node;
verifying the first node and the second node;
respectively acquiring a first view angle area of the first node and a second view angle area of the second node;
acquiring the overlapping rate of the first visual angle area and the second visual angle area;
respectively acquiring a first characteristic value of the first node and a second characteristic value of the second node;
simultaneously and respectively acquiring a first suitable range of the first node and a second suitable range of the second node;
respectively adjusting a first upper limit value of the first proper range and a second upper limit value of the second proper range according to a preset upper adjustment range;
simultaneously, respectively adjusting a first lower limit value of the first suitable range and a second lower limit value of the second suitable range according to preset down-regulation amplitude;
respectively acquiring a first adjusting range after the first suitable range is adjusted and a second adjusting range after the second suitable range is adjusted;
when the overlapping rate is smaller than or equal to a preset overlapping rate threshold value, the first characteristic value is within the first adjusting range, and the second characteristic value is within the second adjusting range, the first node and the second node pass verification, and the first node and the next second node continue verification;
and when the first node and all the second nodes in the target collection node set pass the verification, the validity of the first node passes the verification.
6. A system for remote acquisition of image data, comprising:
the receiving module is used for receiving a target object input by a user;
the first acquisition module is used for acquiring a target acquisition node set associated with the target object;
the second acquisition module is used for acquiring a target image through each target acquisition node in the target acquisition node set;
the third acquisition module is used for acquiring the splicing rule corresponding to the target acquisition node set;
and the splicing and output module is used for splicing the target images based on the splicing rule to serve as image data and then outputting the image data.
7. The system of claim 6, wherein the first acquisition module performs operations comprising:
acquiring a position area corresponding to the target object;
acquiring a preset association rule, and acquiring a first association position area and a second association position area associated with the position area based on the association rule;
acquiring a first acquisition node set corresponding to the position area in a preset acquisition node database;
acquiring a second acquisition node set corresponding to the first associated position area and a third acquisition node set corresponding to the second associated position area in the acquisition node database;
and integrating the first collection node set, the second collection node set and the third collection node set to obtain the target collection node set.
8. The system for remotely acquiring image data as recited in claim 6, further comprising:
the verification and replacement module is used for verifying the state performance of each target acquisition node in the target acquisition node set before a target image is acquired through each target acquisition node in the target acquisition node set, and replacing target acquisition nodes which fail to be verified in the target acquisition node set with corresponding preset standby nodes;
the verification and replacement module performs operations comprising:
acquiring a preset state performance detection model, detecting each target acquisition node for multiple times by adopting the state performance detection model, outputting a detection feedback value after each detection, and storing the detection feedback value into a preset detection feedback value list;
calculating a first verification index of each target acquisition node based on the detection feedback value list, wherein the calculation formula is as follows:
wherein, mu1,iThe first verification index, α, for the ith target collection nodefullIs a preset first verification index full mark threshold value, e is a natural constant, niThe total number, sigma, of the detection feedback values corresponding to the ith target collection node in the detection feedback value listi,tFor the t-th detection feedback value, sigma, in the detection feedback values corresponding to the ith target acquisition node in the detection feedback value list0Is a preset detection feedback value threshold value;
and when the first verification index is larger than or equal to a preset first verification index threshold value, the state performance corresponding to the target acquisition node passes the verification, otherwise, the state performance does not pass the verification.
9. The system for remotely acquiring image data as recited in claim 6, further comprising:
the stability verification module is used for acquiring a current state value and a state value record of each target acquisition node when a target image is acquired through each target acquisition node in the target acquisition node set, acquiring a preset prediction model at the same time, predicting a prediction state value corresponding to each target acquisition node based on the state value record by adopting the prediction model, verifying the stability of each target acquisition node based on the current state value and the prediction state value, and timely reminding a user if the target acquisition node which is not verified exists;
the stability verification module performs operations comprising:
calculating a second validation index for each of the target collection nodes based on the current state value and the predicted state value, the calculation formula being as follows:
wherein, mu2,iThe second verification index, p, for the ith target acquisition nodefullIs a preset second verification index full mark threshold value, e is a natural constant, diZ is the total number of times the status value record corresponding to the ith target collection node is predicted using the prediction modeli,0For the current state value, z, of the ith target collection nodei,xThe predicted state value Z obtained by performing the x-th prediction on the state value record corresponding to the ith target acquisition node by using the prediction model0Is a preset detection threshold value, tau is a preset error coefficient, epsilon1And ε2The weight value is a preset weight value;
and when the second verification index is larger than or equal to a preset second verification index threshold value, the stability corresponding to the target acquisition node passes the verification, otherwise, the stability does not pass the verification.
10. The system for remotely acquiring image data as recited in claim 6, further comprising:
the verification and rejection module is used for verifying the effectiveness of each target acquisition node in the target acquisition node set before acquiring a target image through each target acquisition node in the target acquisition node set, and rejecting the target acquisition nodes which fail to be verified in the target acquisition nodes;
the verification and rejection module executes operations comprising:
taking any one target acquisition node in the target acquisition node set as a first node, and simultaneously taking another target acquisition node in the target acquisition node set as a second node;
verifying the first node and the second node;
respectively acquiring a first view angle area of the first node and a second view angle area of the second node;
acquiring the overlapping rate of the first visual angle area and the second visual angle area;
respectively acquiring a first characteristic value of the first node and a second characteristic value of the second node;
simultaneously and respectively acquiring a first suitable range of the first node and a second suitable range of the second node;
respectively adjusting a first upper limit value of the first proper range and a second upper limit value of the second proper range according to a preset upper adjustment range;
simultaneously, respectively adjusting a first lower limit value of the first suitable range and a second lower limit value of the second suitable range according to preset down-regulation amplitude;
respectively acquiring a first adjusting range after the first suitable range is adjusted and a second adjusting range after the second suitable range is adjusted;
when the overlapping rate is smaller than or equal to a preset overlapping rate threshold value, the first characteristic value is within the first adjusting range, and the second characteristic value is within the second adjusting range, the first node and the second node pass verification, and the first node and the next second node continue verification;
and when the first node and all the second nodes in the target collection node set pass the verification, the validity of the first node passes the verification.
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| CN112215037A (en) * | 2019-07-10 | 2021-01-12 | 浙江宇视科技有限公司 | Object tracking method and device, electronic equipment and computer readable storage medium |
| WO2021012791A1 (en) * | 2019-07-22 | 2021-01-28 | 平安科技（深圳）有限公司 | Face login method, apparatus, computer device and storage medium |
| CN111046121A (en) * | 2019-12-05 | 2020-04-21 | 亿利生态大数据有限公司 | Environment monitoring method, device and system |
| CN112511767A (en) * | 2020-10-30 | 2021-03-16 | 济南浪潮高新科技投资发展有限公司 | Video splicing method and device, and storage medium |
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